Hugging Face Trending Papers
Protegrity-PII Technical Report
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arXiv Computation and Language
Oct 23
Protegrity-PII Technical Report
arXiv:2610. 30518v1 Announce Type: new Abstract: We present Protegrity-PII, a new PII decision model for structured and unstructured text.
arXiv Computation and Language
Sep 14
All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing
arXiv:2410.17355v4 Announce Type: replace Abstract: Due to their capacity to acquire world knowledge from large corpora, pre-trained language models (PLMs) are extensively used in ultra-fine entity t...
arXiv Computation and Language
Sep 16
SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP
SciNLP is a new benchmark dataset for full‑text entity and relation extraction in the NLP domain, comprising 60 manually annotated papers with 6,429 entities and 1,649 relations. It is the first dataset to provide full‑text annotations of entities and their relationships specifically for NLP literature. Experiments show that models trained on SciNLP outperform baselines on certain tasks, and the dataset enabled the automatic construction of a fine‑grained knowledge graph with an average node degree of 3.3.
arXiv AI
Aug 11
Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach
arXiv:2608. 08636v1 Announce Type: cross Abstract: Scientific named entity recognition (SciNER) plays a crucial role in information extraction and knowledge discovery from scientific texts.
arXiv Computation and Language
Oct 12
Assessment of Generative Named Entity Recognition in the Era of Large Language Models
The paper evaluates open‑source large language models (LLMs) for both flat and nested named entity recognition (NER) tasks, comparing them to traditional encoder‑based models and decoder‑based LLMs using in‑context learning. It shows that with parameter‑efficient fine‑tuning and structured output formats (e.g., inline brackets or XML), LLMs can match or exceed the performance of conventional NER systems, and that their NER abilities arise from instruction following rather than memorization. Additionally, fine‑tuning for NER minimally affects, and can even improve, the LLMs’ general capabilities on other benchmarks.
arXiv AI
Oct 15
Calibrated Typed Decisions for Entity, Relation, and Event Extraction
arXiv:2610.17113v1 Announce Type: cross Abstract: Information extraction models often perform well on the datasets they are trained on, but transferring to new domains and annotation guidelines remai...